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Record W2111095151 · doi:10.1109/tvcg.2010.52

Measurement-Based Modeling of Contact Forces and Textures for Haptic Rendering

2010· article· en· W2111095151 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueIEEE Transactions on Visualization and Computer Graphics · 2010
Typearticle
Languageen
FieldNeuroscience
TopicTactile and Sensory Interactions
Canadian institutionsMcGill UniversityUniversity of Ottawa
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsHaptic technologyComputer scienceComputer visionDisplacement mappingPolygon meshStiffnessArtificial intelligenceRendering (computer graphics)Surface finishMobile deviceDisplacement (psychology)Texture mappingComputer graphics (images)Surface roughnessMaterials science

Abstract

fetched live from OpenAlex

Haptic texture represents the fine-grained attributes of an object's surface and is related to physical characteristics such as roughness and stiffness. We introduce an interactive and mobile scanning system for the acquisition and synthesis of haptic textures that consists of a visually tracked handheld touch probe. The most novel aspect of our work is an estimation method for the contact stiffness of an object based solely on the acceleration and forces measured during stroking of its surface with the handheld probe. We establish an experimental relationship between the estimated stiffness and the contact stiffness observed during compression. We also measure the height-displacement profile of an object's surface enabling us to generate haptic textures. We show an example of mapping the textures on to a coarse surface mesh obtained with an image-based technique, but the textures may also be combined with coarse surface meshes obtained by manual modeling.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.816
Threshold uncertainty score0.451

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.062
GPT teacher head0.302
Teacher spread0.240 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it